text: from copy import deepcopy
  from ding.entry import serial_pipeline
  from easydict import EasyDict
  
  qbert_acer_config = dict(
      env=dict(
          collector_env_num=16,
          evaluator_env_num=4,
          n_evaluator_episode=8,
          stop_value=1000000,
          env_id='QbertNoFrameskip-v4',
          frame_stack=4,
          manager=dict(shared_memory=False, )
      ),
      policy=dict(
          cuda=True,
          priority=False,
          random_collect_size=1000,
          model=dict(
              obs_shape=[4, 84, 84],
              action_shape=6,
              encoder_hidden_size_list=[128, 128, 512],
              critic_head_hidden_size=512,
              critic_head_layer_num=2,
              actor_head_hidden_size=512,
              actor_head_layer_num=2,
          ),
          unroll_len=32,
          learn=dict(
              # (int) collect n_sample data, train model update_per_collect times
              # here we follow impala serial pipeline
              update_per_collect=10,
              # (int) the number of data for a train iteration
              batch_size=32,
              # grad_clip_type='clip_norm',
              # clip_value=10,
              learning_rate_actor=0.0001,
              learning_rate_critic=0.0003,
              # (float) loss weight of the value network, the weight of policy network is set to 1
              # (float) loss weight of the entropy regularization, the weight of policy network is set to 1
              entropy_weight=0.01,
              # (float) discount factor for future reward, defaults int [0, 1]
              discount_factor=0.99,
              # (float) additional discounting parameter
              trust_region=True,
              # (float) clip ratio of importance weights
              # (float) clip ratio of importance weights
              c_clip_ratio=10,
          ),
          collect=dict(
              # (int) collect n_sample data, train model n_iteration times
              n_sample=16,
              # (float) discount factor for future reward, defaults int [0, 1]
              discount_factor=0.99,
              collector=dict(collect_print_freq=1000, ),
          ),
          eval=dict(evaluator=dict(eval_freq=1000, )),
          other=dict(replay_buffer=dict(
              type='naive',
              replay_buffer_size=10000,
          ), ),
      ),
  )
  main_config = EasyDict(qbert_acer_config)
  
  qbert_acer_create_config = dict(
      env=dict(
          type='atari',
          import_names=['dizoo.atari.envs.atari_env'],
      ),
      env_manager=dict(type='subprocess'),
      policy=dict(type='acer'),
  )
  create_config = EasyDict(qbert_acer_create_config)
  
  if __name__ == '__main__':
      from ding.entry import serial_pipeline
      serial_pipeline((main_config, create_config), seed=0)
  